In newer versions of pandas (0.20.0+, I believe), this can be done directly:

df.to_json('temp.json', orient='records', lines=True)

Direct compression is also possible:

df.to_json('temp.json.gz', orient='records', lines=True, compression='gzip')

The format of the JSON string is just how the output will look like for every parameter as following

• split : dict like {index -> [index], columns -> [columns], data -> [values]}
• records : list like [{column -> value}, ... , {column -> value}]
• index : dict like {index -> {column -> value}}
• columns : dict like {column -> {index -> value}}
• values : just the values array

Your case seems to not fit to any orientation available in read_json.

To comprehend when use each orientation look up e.g. examples in the documentation of this method (https://pandas.pydata.org/docs/reference/api/pandas.read_json.html).

In your case you could start from:

result = pd.json_normalize(json.loads(data))

(import json needed).

This function at least properly drills down the structure of your JSON input.

A similar approach, omitting the first level (glossary) from column names, is:

wrk = pd.read_json(data)
df = pd.json_normalize(wrk.glossary)

By default df.to_json uses epoch date format. But you can control it by using date_format argument.

date_format

Type of date conversion. ‘epoch’ = epoch milliseconds, ‘iso’ = ISO8601. The default depends on the orient. For orient='table', the default is ‘iso’. For all other orients, the default is ‘epoch’.

df.to_json(orient="records", date_format="iso")